Machine Learning
How Global is Predictability?
November 3, 2023
We show that asset pricing has a strong global component in the sense that a common global model has stronger predictability of stock returns than local models estimated in each country – even when the global model is estimated without the use of local data. Nevertheless, asset pricing has a small local component – in order to detect it, we develop a refined transfer learning model that gains power and precision by building off the global component.
Fixed Income
Corporate Bond Factors: Replication Failures and a New Framework
October 5, 2023
We demonstrate that the literature on corporate bond factors suffers from replication failures, inconsistent methodological choices, and the lack of a common error-free dataset. Going beyond identifying this replication crisis, we create a clean database of corporate bond returns where outliers are analyzed individually and propose a robust factor construction.
Tax Aware
Loss Harvesting or Gain Deferral? A Surprising Source of Tax Benefits of Tax-Aware Long-Short Strategies
September 26, 2023
We explore the mechanism for how tax-aware long-short factor strategies, within their first three years since inception, can realize cumulative net capital losses exceeding 100% of initially invested capital, all while generating a significant pre-tax alpha – a result shown in previous research. Surprisingly, we find in these strategies that net capital losses arise not from an increased realization of capital losses but rather from the deferral of capital gains, especially short-term gains on long positions.
Machine Learning
Financial Machine Learning
August 1, 2023
In this survey the nascent literature on machine learning in financial markets, we highlight the best examples of what this line of research has to offer and recommend promising directions for future research.
ESG Investing
Is Capital Structure Irrelevant with ESG Investors?
September 23, 2022
We examine whether capital structure is irrelevant for enterprise value and investment when investors care about ESG issues, which we denote “ESG-Modigliani-Miller” (ESG-MM). Theoretically, we show that ESG-MM holds if ESG is additive and markets are perfect. Empirically, we provide evidence of failure of ESG-MM, implying that firms and governments can exploit non-additive ESG or segmented markets.
Machine Learning
Machine Learning and the Implementable Efficient Frontier
August 18, 2022
We propose that investment strategies should be evaluated based on their net-of-trading-cost return for each level of risk, which we term the "implementable efficient frontier." While numerous studies use machine learning return forecasts to generate portfolios, their agnosticism toward trading costs leads to excessive reliance on fleeting small-scale characteristics, resulting in poor net returns. We develop a framework that produces a superior frontier by integrating trading-cost-aware portfolio optimization with machine learning
Tax Aware
When Fortune Doesn’t Favor the Bold: Perils of Volatility for Wealth Growth and Preservation
May 12, 2022
Entrepreneurs and executives holding much of their wealth in a highly appreciated single stock face either the high risk of idiosyncratic volatility and potentially catastrophic losses, or selling stock and facing an immediate, punitive tax burden. This paper evaluates this choice and explains how it relates to classic betting strategies and economic theory, finding tax-efficient techniques might strike the balance between the urgency to diversify concentrated risk and aversion to taxes.
Factor/Style Investing
Pricing Without Mispricing
July 16, 2021
We offer a novel test of whether an asset pricing model describes expected returns in the absence of mispricing. Our test assumes such a model assigns zero alpha to investment strategies using decade-old information. Prominent multifactor models do not satisfy this condition – while multifactor betas help capture current expected returns on mispriced stocks, persistence in those betas distorts the stocks' implied expected returns after prices correct.
Factor/Style Investing
What Can Betting Markets Tell Us About Investor Preferences and Beliefs? Implications for Low Risk Anomalies
June 30, 2021
We relate the low risk anomaly in financial markets to the Favorite-Longshot Bias in betting markets and provide novel evidence to both anomalies. Synthesizing the evidence, we study the joint implications from the two settings for a unifying explanation. Rational theories of risk-averse investors with homogeneous beliefs cannot explain the cross-sectional relationship between diversifiable risk and return in betting markets. Rather, we appeal to models of non-traditional preferences or heterogeneous beliefs.
Factor/Style Investing
What Can Betting Markets Tell Us About Investor Preferences and Beliefs? Implications for Low Risk Anomalies
May 13, 2021
We relate the low risk anomaly in financial markets to the Favorite-Longshot Bias in betting markets and provide novel evidence to both anomalies. Synthesizing the evidence, we study the joint implications from the two settings for a unifying explanation. Rational theories of risk-averse investors with homogeneous beliefs cannot explain the cross-sectional relationship between diversifiable risk and return in betting markets. Rather, we appeal to models of non-traditional preferences or heterogeneous beliefs.